Fetal dermal mesenchymal stem cells (FDMSCs), isolated from fetal skin, are serving as a novel MSC candidate with great potential in regenerative medicine. More recently, the paracrine actions, especially MSC-derived exosomes, are being focused on the vital role in MSC-based cellular therapy. This study was to evaluate the therapeutic potential of exosomes secreted by FDMSCs in normal wound healing. First, the in vivo study indicated that FDMSC exosomes could accelerate wound closure in a mouse full-thickness skin wound model. Then, we investigated the role of FDMSC-derived exosomes on adult dermal fibroblast (ADFs). The results demonstrated that FDMSC exosomes could induce the proliferation, migration, and secretion of ADFs. We discovered that after treatment of exosomes, the Notch signaling pathway was activated. Then, we found that in FDMSC exosomes, the ligands of the Notch pathway were undetectable expect for Jagged 1, and the results of Jagged 1 mimic by peptide and knockdown by siRNA suggested that Jagged 1 may lead the activation of the Notch signal in ADFs. Collectively, our findings indicated that the FDMSC exosomes may promote wound healing by activating the ADF cell motility and secretion ability via the Notch signaling pathway, providing new aspects for the therapeutic strategy of FDMSC-derived exosomes for the treatment of skin wounds.
As a crucial part of the Intelligent Transportation System, traffic forecasting is of great help for traffic management and guidance. However, predicting short-term traffic conditions on a large-scale road network is challenging due to the complex spatio-temporal dependencies found in traffic data. Previous studies used Euclidean proximity or topological adjacency to explore the spatial correlation of traffic flows, but did not consider the higher-order connectivity patterns exhibited in a road network, which have a significant influence on traffic propagation. Meanwhile, traffic sequences display distinct multiple timefrequency properties, yet few researchers have made full use of this resource. To fill this gap, we propose a novel hybrid framework-Wavelet-based Higher-order Spatial-Temporal method (Wavelet-HST) to accurately predict network-scale traffic speeds. Wavelet-HST first uses discrete wavelet transform (DWT) to decompose raw traffic data into several components with different frequency sub-bands. Then a motifbased graph convolutional recurrent neural network (Motif-GCRNN) is proposed to learn the higherorder spatio-temporal dependencies of traffic speeds from low-frequency components, and auto-regressive moving average (ARMA) models are employed to simulate random fluctuations from the high-frequency components. We evaluate the framework on a traffic dataset collected in Chengdu, China, and experimental results demonstrate that Wavelet-HST outperforms six state-of-art prediction methods by an improvement of 7.8% ∼10.5% in the root mean square error. INDEX TERMS Traffic prediction, graph convolutional network (GCN), spatio-temporal modeling, higherorder connectivity patterns, wavelet transform, time-frequency properties.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.